Mainly around bounding variables, especially when coupled with overly ambitious scope/number of tests. The short version is that its really easy to wind up with say a 80 experiment test where half or more of the combinations are invalid or perform poorly to the degree they don't adequately lend confidence to the prediction in the region of the global maximum. Leads to wasted work and the poor predictive ability doesn't lead well into focused future work. There are also the usual statistical foot guns of p-hacking and such.
For a concrete example, consider a bread baking optimization, considering time, temperature, and baking soda %. This gives a cubic design space, for which the naively (ignoring expected covariance of temperature*time) optimal design for 9 experiments is the cube's corners + the center point. If, for instance, your predetermined t_max always results in a briquette instead of a loaf, ~half of your experimental data is going to be worthless.
With more nuance, and temp_max is genuinely the highest reasonable temperature there are still two problems:
a. covariance is likely to drive combinations into 'invalid' territory (e.g. temp_max + time_max is likely to be invalid, or 2/9ths of your experimental. temp_min / time_min is also likely invalid for another 2/9ths).
b. predictive power / linearity of response over the ranges specified. Even if the combinations aren't invalid, if they are all the min/max combinations are poorly performing (due to overly wide, but valid, boundaries) you can wind up with poor predictive performance.
Covariance can be accounted for, design space trimmed (e.g. to a cube with a corner or two cut off), and bounds set conservatively but that is all tricky manual intervention that relies on knowledge of the problem domain and scaling factors of the underlying physics. When the problem isn't well understood it is easy to make errors in assumptions, those errors have a high cost, and if the problem was well understood a DOE probably wouldn't be necessary.
edit: for a less trivial example of a suitable problem for a d-optimal DOE, but with tricky bounding / underlying physics, consider: a 4 part formulation of fumed silica, cyanoacrylate, isopropanol, and water to make a gap-filling/quick-setting adhesive.